Unmanned aerial vehicle path planning method and system based on intelligent logistics cabinet

Abstract

The invention provides an unmanned aerial vehicle path planning method and system based on an intelligent logistics cabinet, and relates to the field of logistics planning, and the method comprises the steps: obtaining a distribution task set, and calculating the total travel time corresponding to all distribution tasks in the distribution task set, so as to determine a scheduling time range; grouping the distribution task set to obtain a plurality of task groups; configuring constraint conditions for modeling; introducing a binary variable and using a time extension network method to introduce a time dimension to construct a target planning model; and solving the target planning model through a preset solver. Through time discretization, a dynamic problem is converted into a static network problem, so that the problem is more visual and easy to understand and analyze. The target planning model is easy to expand, the problem that a continuous time model is difficult to model can be converted into the problem that modeling can be carried out, and modeling and solving of complex problems are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of logistics planning, and specifically to a method and system for drone path planning based on a smart logistics cabinet. Background Art

[0002] With the development of smart logistics and drone technology, driven by factors such as the sharp increase in distribution demand, soaring labor costs, and complex service scenarios, drones have gradually become an indispensable infrastructure for the modern logistics industry, helping the logistics industry to achieve leapfrog development. At present, a new drone delivery method based on smart logistics cabinets is emerging. The coordinated delivery of logistics cabinets and drones as a "non-contact" delivery can effectively respond to emergencies, such as effectively reducing contact between people during the epidemic.

[0003] In the related technology, the top of the logistics cabinet is equipped with an automatic loading and unloading device for packages, and serves as a take-off and landing platform for drones. The drone automatically completes the loading of packages on the top of the logistics cabinet, and automatically completes the unloading of packages and the replenishment of electricity after flying to the top of the designated logistics cabinet. Drone delivery based on smart logistics cabinets is a highly automated process. Drones perform delivery tasks by carrying packages and flying between different logistics cabinets. The efficiency of the entire delivery process depends on the drone's path planning.

[0004] However, in order to plan the driving route of drone delivery, it is necessary to establish a task index to analyze the drone task scheduling process. The entire modeling process is complex and difficult to solve. Therefore, a better path planning method is urgently needed to improve the efficiency of collaborative delivery between logistics cabinets and drones. Summary of the invention

[0005] In response to the shortcomings of the prior art, the present application provides a drone path planning method and system based on smart logistics cabinets, which solves the current problem of difficulty in modeling and solving complex problems when planning paths for collaborative delivery of logistics cabinets and drones.

[0006] To achieve the above objectives, this application is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a drone path planning method based on a smart logistics cabinet, the drone path planning method comprising: obtaining a delivery task set, and calculating the total travel time corresponding to all delivery tasks in the delivery task set to determine a scheduling time range; The delivery task set is grouped to obtain multiple task groups, and the starting and ending logistics cabinets of the delivery tasks in the same task group are the same; the constraints for modeling are configured, and the constraints include target constraints, time constraints, path constraints, task constraints, variable constraints and take-off and landing constraints that characterize the completion time; with the goal of minimizing the completion time, binary variables are introduced and the time dimension is introduced using the time extended network method to construct a target planning model, so as to transform the UAV task scheduling process into an arc selection process in the time extended network; the target planning model is solved by a preset solver to obtain multiple flight arcs and holding arcs to determine the path planning network for the UAV to perform delivery tasks between multiple smart logistics cabinets.

[0007] According to the first aspect of the embodiment of the present application, the aforementioned objective of minimizing the completion time is to introduce binary variables and use the time extension network method to introduce the time dimension to construct a target planning model, which can specifically include the following steps: discretizing the scheduling time range into multiple time points with sufficiently small intervals and equal intervals; creating nodes based on each logistics cabinet and each time point to obtain multiple nodes connected by arcs; for each time point, creating a holding arc for each logistics cabinet, and creating a flight arc from one logistics cabinet to another logistics cabinet to characterize the flight path of the drone from one logistics cabinet to another logistics cabinet; both the flight arc and the holding arc correspond to a head node and a tail node; introducing a binary variable to characterize whether each arc is selected, and when the introduced binary variable When Be chosen, represents the index of the arc, is the set of all arcs, when When Not selected.

[0008] According to the first aspect of the embodiment of the present application, the target planning model is solved by a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets. Specifically, the following steps may be included: In the case of, based on the preset first strategy and second strategy, the holding arc is mapped to each flight arc, and the arc connected to the flight arc is selected. Multiple preceding holding arcs of the head node, and connected to the arcs Multiple subsequent holding arcs of the tail node; is the flight arc set, Represents the index of the arc.

[0009] The first strategy includes: for the flight arc , add a set of preceding holding arcs , in the selection arc In the case of The corresponding holding arc is determined in order to represent the take-off action of the UAV on the corresponding logistics cabinet; The second strategy includes: Append a set of subsequent hold arcs , in the selection arc In the case of The corresponding holding arc is determined in order to characterize the landing action of the UAV on the corresponding logistics cabinet.

[0010] According to the first aspect of the embodiment of the present application, for a time point , if there is a time point , so that , then at the node and Create a flight arc between ,in, Indicates that the drone arrive Flight time; Indicates the starting point logistics cabinet, Indicates the destination logistics cabinet. , , Assemble for logistics cabinets; The path planning network satisfies the expression: ,in, To plan the path network, Represents a collection of nodes. represents a collection of arcs, and Represent the collection of holding arcs and flying arcs respectively.

[0011] According to the first aspect of the embodiment of the present application, the completion time of multiple task groups includes the take-off action preparation time and the landing action preparation time of the drone; when the number of configured flight arcs from the starting logistics cabinet to the end logistics cabinet is greater than or equal to the number of tasks in the task group, it is determined that all tasks can be executed; The objective constraint satisfies the expression: ,in, represents the maximum time required for all drones to complete their assigned tasks; The time constraint satisfies the expression: In the formula, It is an arc The tail node of For Node The corresponding time point is used to indicate that the drone is at the node Corresponding logistics cabinet The time to complete the landing, is a binary variable, Indicates the preparation time required to complete a flight action. The corresponding flight arc indicates that the UAV performs the corresponding flight action; the time constraint guarantees At least with any selected flight arc The completion time is the same.

[0012] According to a first aspect of an embodiment of the present application, the path constraint includes a first path constraint, a second path constraint, a third path constraint, and a fourth path constraint; The first path constraint satisfies the expression: In the formula, Represents a node, And it means drone The starting node of , Indicates the index of the drone, Indicates the logistics cabinet where the drone initially stops. It is a collection of drones. Represents all outgoing nodes The first path constraint is used to ensure that each drone starts its mission from the logistics cabinet where it initially stops as the starting node; The second path constraint satisfies the expression: In the formula, Indicates the inflow node The arc set of , Indicates that except for the source node and precipitation nodes The set of intermediate nodes other than Represents the total number of nodes; the second path constraint is used to ensure that for each intermediate node ,The number of its inflow arcs must be equal to the number of outflow arcs, i.e. the drone must leave after reaching an intermediate node to ensure the conservation of the number of drones; The third path constraint satisfies the expression: The third path constraint is used to ensure that for each intermediate node , at most one arc flowing into it is selected to ensure that the UAV can only choose one path to execute at any intermediate node, avoiding multiple paths to ensure the uniqueness and certainty of the path; The fourth path constraint satisfies the expression: The fourth path constraint is used to ensure that the UAV eventually flows into the settling node and ends its flight action, and ensures that the number of all arcs flowing into the settling node must be equal to the total number of UAVs, and that each UAV can be effectively scheduled.

[0013] According to a first aspect of the embodiment of the present application, a variable constraint is used to define a value range of a variable and satisfies the expression: in, , , are binary variables, represents an arc, represents a collection of arcs, The corresponding holding arc indicates that the drone stays on the corresponding logistics cabinet for a period of time; The task constraint satisfies the expression: In the formula, Represents a task group, Represents the task group The corresponding arc set, represents the set of all task groups, Represents the number of tasks in a task group; task constraints are used to ensure that each task group The number of selected flight arcs is at least equal to the number of each task group The number of tasks to ensure that all tasks are executed.

[0014] According to a first aspect of an embodiment of the present application, the takeoff and landing constraint includes a first takeoff and landing constraint and a second takeoff and landing constraint; wherein the first takeoff and landing constraint is used to determine that if a flight arc is selected, an additional holding arc must be selected to ensure that the takeoff and landing actions must be performed together with the execution flight segment; the first takeoff and landing constraint satisfies the expression: In the formula, , and is a binary variable, indicating that if the arc is maintained If selected, it is equal to 1, otherwise it is equal to 0; is the set of holding arcs connected to the flying arc head node, is the set of holding arcs connected to the tail node of the flight arc; and Used to distinguish between flying arc and holding arc, and This means that if a drone flies, it must take off and land. Used to distinguish the types of arcs, and Respectively belong and ; The second takeoff and landing constraint is used to determine that at most one preparation action can be performed on the same holding arc. The drone cannot take off and land at the same time to ensure that the model can correctly arrange the takeoff and landing sequence of the drone on the logistics cabinet to avoid docking conflicts, thereby ensuring the order of operations; The second takeoff and landing constraint satisfies the expression: 。

[0015] In a second aspect, an embodiment of the present application provides a drone path planning system based on a smart logistics cabinet, the drone path planning system comprising: an acquisition module, a grouping module, a constraint configuration module, a construction module and a solution module; wherein, The acquisition module is used to obtain the delivery task set and calculate the total travel time corresponding to all delivery tasks in the delivery task set to determine the scheduling time range; The grouping module is used to group the delivery task set to obtain multiple task groups. The starting and ending logistics cabinets of the delivery tasks in the same task group are the same. The constraint configuration module is used to configure the constraint conditions used for modeling, and the constraint conditions include target constraints representing completion time, time constraints, path constraints, task constraints, variable constraints, and takeoff and landing constraints; The building module is used to minimize the completion time, introduce binary variables and use the time extension network method to introduce the time dimension to build a goal planning model, so as to transform the UAV task scheduling process into an arc selection process in the time extension network; The solving module is used to solve the target planning model through a preset solver to obtain multiple flight arcs and holding arcs to determine the path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the drone path planning method based on the smart logistics cabinet in the aforementioned first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the drone path planning method based on the smart logistics cabinet in the aforementioned first aspect is implemented.

[0018] This application provides a drone path planning method and system based on a smart logistics cabinet. Compared with the prior art, it has the following beneficial effects: This application analyzes the goal of minimizing the completion time, introduces binary variables and uses the time extension network method to introduce the time dimension to build a goal planning model, transforms the task scheduling of drones into an arc selection problem in a time extension network, and performs task planning based on nodes and arcs without using task drone indexes or task indexes; this application discretizes time and transforms dynamic problems into static network problems, making the problem more intuitive, easy to understand and analyze. The goal planning model is easy to expand and can transform problems that are difficult to model with continuous time models into problems that can be modeled, thereby achieving modeling and solving of complex problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flowchart of a method for drone path planning based on a smart logistics cabinet provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the coordinated delivery of logistics cabinets and drones provided in an embodiment of the present application; Figure 3 is a schematic diagram of a time expansion network provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of a drone path planning system based on a smart logistics cabinet provided in an embodiment of the present application; Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0022] The embodiments of the present application provide a drone path planning method and system based on a smart logistics cabinet, thereby solving the current problem of difficulty in modeling and solving complex problems when performing path planning for collaborative delivery between logistics cabinets and drones.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The following first introduces a drone path planning method based on a smart logistics cabinet provided in an embodiment of the present application.

[0025] The present application embodiment provides a flow chart of a method for drone path planning based on a smart logistics cabinet, such as Figure 1 As shown, the UAV path planning method may include the following steps S110-S150.

[0026] S110, obtain a delivery task set, and calculate the total travel time corresponding to all delivery tasks in the delivery task set to determine the scheduling time range. It can be understood that the scheduling time range is ,in, is the total travel time.

[0027] S120: Group the delivery task set to obtain multiple task groups, wherein the starting point logistics cabinet and the end point logistics cabinet of the delivery tasks in the same task group are the same.

[0028] S130 , configuring constraint conditions for modeling, wherein the constraint conditions include target constraints representing completion time, time constraints, path constraints, task constraints, variable constraints, and takeoff and landing constraints.

[0029] S140, with the goal of minimizing the completion time, binary variables are introduced and the time dimension is introduced using the time extension network method to construct a goal planning model, so as to transform the UAV task scheduling process into an arc selection process in the time extension network.

[0030] S150. Solve the target planning model through a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets.

[0031] The above is a specific implementation method of a drone path planning method based on a smart logistics cabinet provided by an embodiment of the present application. It should be noted that the time-extended network is a mathematical tool for modeling and analyzing dynamic networks; the time-extended network transforms the changes of dynamic networks into the form of static networks by introducing the time dimension, thereby facilitating the application of traditional graph theory methods for analysis and processing. This application assumes that drones are homogeneous and that there is no time window restriction on tasks, so the model can be constructed without adding drone and task indexes.

[0032] It is understandable that this application analyzes the goal of minimizing the completion time, introduces binary variables and uses the time extension network method to introduce the time dimension to construct a target planning model, converts the task scheduling of drones into an arc selection problem in a time extension network, and performs task planning based on nodes and arcs without using task drone indexes or task indexes; creates a suitable scheduling plan for drones to ensure that all tasks are completed and there are no drone docking conflicts on each logistics cabinet. The target planning model is a time extension network model, which discretizes time and converts dynamic problems into static network problems, making the problem more intuitive, easy to understand and analyze.

[0033] This application uses the time-extended network method to model the path planning of drones based on smart logistics cabinets, which can create a suitable scheduling plan for drones, ensure that all tasks can be executed, and improve delivery efficiency. The target planning model is easy to expand. Although the time-extended network will increase the use of computing resources compared to the continuous-time model, the time-extended network can transform problems that are difficult to model with the continuous-time model into problems that can be modeled.

[0034] It should be noted that after the mathematical model of the target programming model is established, a solver can be used to directly solve, for example: IBM ILOG CPLEX, Gurobi, OR-Tools, etc. Exemplarily, the present invention can use IBMPLUG CPLEX software to solve, using the Java programming language, and running on a personal computer equipped with an i9-13900 k CPU, 64 GB RAM and Windows 10 Professional operating system. Experimental verification shows that the CPLEX solver can find the optimal solution on a certain scale uniform instance, and the optimal target values ​​of these instances range from 21 minutes to 91 minutes, with an average of 41 minutes. It is proved that the time expansion network model proposed in the present invention is feasible.

[0035] In one example, the path planning network satisfies the expression: ,in, To plan the network for the path, Represents a collection of nodes. represents a collection of arcs, and It can be understood that the present application transforms the logistics UAV task scheduling problem into an arc selection problem in a time-extended network, and the flight path of the UAV between logistics cabinets can be represented by a series of arcs in the network.

[0036] In another example, with Represents a task group, Represents a collection of task groups; and Respectively represent task groups If the number of flight arcs from the starting logistics cabinet to the end logistics cabinet is greater than or equal to the task group If the number of tasks in is greater than , it means that all tasks can be executed; this method does not require a specific drone to perform a specified task, as long as the number of arcs to be solved meets the task number requirement.

[0037] In some embodiments, the aforementioned minimization of completion time is taken as the goal, binary variables are introduced, and the time dimension is introduced using the time extension network method to construct a target planning model, so as to transform the UAV task scheduling process into an arc selection process in the time extension network, that is, the aforementioned S140 may specifically include the following steps: S210, discretizing the scheduling time range into multiple time points with sufficiently small intervals and equal intervals; S220, creating a node based on each logistics cabinet and each time point to obtain a plurality of nodes connected by arcs; S230. For each time point, create a holding arc for each logistics cabinet, and create a flight arc from one logistics cabinet to another logistics cabinet to represent the flight path of the drone from one logistics cabinet to another logistics cabinet; both the flight arc and the holding arc correspond to a head node and a tail node; S240, introduce binary variables to represent whether each arc is selected. When Be chosen, represents the index of the arc, is the set of all arcs, when When Not selected.

[0038] It is understandable that for each logistics cabinet and time point , create a node ; For each time point , for each logistics cabinet Create a hold arc ; If this hold arc is subsequently selected, it indicates the dwell time (take-off or landing) of the drone on the corresponding logistics cabinet. To another logistics cabinet The flying arc between.

[0039] In one example, for a point in time , if there is a time point , so that , then at the node and Create a flight arc between ,in, Indicates that the drone arrive Flight time; Indicates the starting point logistics cabinet, Indicates the destination logistics cabinet. , , is a set of logistics cabinets. It can be understood that the flight arc is used to represent the flight path of the drone from one logistics cabinet to another, and so on, to build a task delivery network.

[0040] Based on this, this application starts from the time-extended network, creates a series of nodes and arcs, and represents the flight path of the drone between logistics cabinets as a series of node and arc selection problems, proposing a practical solution to the problem of collaborative delivery between logistics cabinets and drones.

[0041] In some embodiments, the completion time of multiple task groups includes the take-off preparation time and the landing preparation time of the drone; when the number of configured flight arcs from the starting logistics cabinet to the ending logistics cabinet is greater than or equal to the number of tasks in the task group, it is determined that all tasks can be executed.

[0042] In the present embodiment, it is understood that since the model objective is to minimize the completion time, the preparation time required for the takeoff and landing of the drone must be added to the completion time of the task. Each takeoff or landing requires a preparation time, and this preparation time is set to ; Therefore, if the flight arc is selected , you must select Connect to Arc A series of preceding holding arcs of the head node, and arcs connected to it A series of subsequent hold arcs for the tail node.

[0043] In some embodiments, the target planning model is solved by a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets, that is, the aforementioned S150 may specifically include the following steps: When selecting a flight arc In the case of, based on the preset first strategy and second strategy, the holding arc is mapped to each flight arc, and the arc connected to the flight arc is selected. Multiple preceding holding arcs of the head node, and connected to the arcs Multiple subsequent holding arcs of the tail node; is the flight arc set, Represents the index of the arc; Specifically, the first strategy includes: for the flight arc , add a set of preceding holding arcs , in the selection arc In the case of The corresponding holding arc is determined in order to characterize the take-off action of the UAV on the corresponding logistics cabinet.

[0044] The second strategy includes: Append a set of subsequent hold arcs , in the selection arc In the case of The corresponding holding arc is determined in order to characterize the landing action of the UAV on the corresponding logistics cabinet.

[0045] In the embodiments of this application, please refer to Figure 2 and Figure 3 It can be understood that in the first strategy, for the flight arc , add a set of leading holding arcs ; ; If you select Arc , you should select The arc in indicates that the drone takes off from the corresponding logistics cabinet; Representing Arc The head node of Representing Arc The tail node of and Respectively, in the node The logistics cabinet and time point at which the

[0046] In the second strategy, Append a set of subsequent hold arcs: ; If Arc is selected , you should select The arc in indicates that the drone performs landing action on the corresponding logistics cabinet.

[0047] In some embodiments, the goal constraint satisfies the expression: ,in, represents the longest time required for all drones to complete their assigned tasks. It can be understood that the goal of this application is to find a task allocation and scheduling method so that The value of is as small as possible.

[0048] In some embodiments, the foregoing time constraints satisfy the expression: In the formula, It is an arc The tail node of For Node The corresponding time point is used to indicate that the drone is at the node Corresponding logistics cabinet The time to complete the landing, is a binary variable, Indicates the preparation time required to complete a flight action. In other words, Indicates the preparation time required for a takeoff and landing action of the drone. The corresponding flight arc indicates that the UAV performs the corresponding flight action; the time constraint guarantees At least with any selected flight arc The completion time is the same.

[0049] In the embodiments of the present application, it can be understood that when When Not selected, at this time Greater than or equal to preparation time , because the arc is maintained has been created, even if there is no drone performing flight actions on the corresponding logistics cabinet, the preparation time It also always exists, but when the flight arc is selected, its corresponding preceding holding arc and subsequent holding arc must be selected, which are used to indicate the take-off time before the drone performs a flight mission and the landing time after completing a mission.

[0050] In addition, if the flight arc Selected , then the flight arc corresponding to the drone The tail node In the logistics cabinet Time to complete landing Plus preparation time Must be less than or equal to the total completion time .

[0051] In some embodiments, the path constraint includes a first path constraint, a second path constraint, a third path constraint, and a fourth path constraint; The first path constraint satisfies the expression: In the formula, Represents a node, And it means drone The starting node of , represents the index of the drone, Indicates the logistics cabinet where the drone initially stops. It is a collection of drones. Represents all outgoing nodes The first path constraint is used to ensure that each drone starts its mission by taking the logistics cabinet where it initially stops as the starting node; The second path constraint satisfies the expression: In the formula, Indicates the inflow node The arc set of , Indicates that except for the source node and precipitation nodes The set of intermediate nodes other than Represents the total number of nodes; the second path constraint is used to ensure that for each intermediate node ,The number of its inflow arcs must be equal to the number of outflow arcs, i.e. the drone must leave after reaching an intermediate node to ensure the conservation of the number of drones; The third path constraint satisfies the expression: The third path constraint is used to ensure that for each intermediate node , at most one arc flowing into it is selected to ensure that the UAV can only choose one path to execute at any intermediate node, avoiding multiple paths to ensure the uniqueness and certainty of the path; The fourth path constraint satisfies the expression: The fourth path constraint is used to ensure that the UAV eventually flows into the settling node and ends its flight action, and ensures that the number of all arcs flowing into the settling node must be equal to the total number of UAVs, and that each UAV can be effectively scheduled.

[0052] In some embodiments, variable constraints are used to define the value range of a variable and satisfy the expression: in, , , are binary variables, represents an arc, represents a collection of arcs, The corresponding holding arc indicates that the drone stays on the corresponding logistics cabinet for a period of time.

[0053] The task constraint satisfies the expression: In the formula, Represents a task group, Represents the task group The corresponding arc set, represents the set of all task groups, Represents the number of tasks in a task group; task constraints are used to ensure that each task group The number of selected flight arcs is at least equal to the number of each task group The number of tasks to ensure that all tasks are executed.

[0054] In some embodiments, the takeoff and landing constraint includes a first takeoff and landing constraint and a second takeoff and landing constraint; wherein the first takeoff and landing constraint is used to determine that if a flight arc is selected, an additional holding arc must be selected to ensure that the takeoff and landing actions must be performed together with the execution flight segment; the first takeoff and landing constraint satisfies the expression: In the formula, , and is a binary variable, indicating that if the arc is maintained If selected, it is equal to 1, otherwise it is equal to 0; is the set of holding arcs connected to the flying arc head node, is the set of holding arcs connected to the tail node of the flight arc; and Used to distinguish between flying arc and holding arc, and This means that if a drone is flying, it must be accompanied by take-off and landing actions. Used to distinguish the types of arcs, and Respectively belong and .

[0055] Understandably, It represents the set of holding arcs before takeoff. It represents the set of holding arcs after landing. For each flight arc If he is selected , then all the flight arcs Related holding arc must also be selected, i.e. or , ensuring that the drone must make necessary stops on the corresponding logistics cabinet before and after performing the mission to complete the preparations for take-off and landing.

[0056] In some embodiments, the second takeoff and landing constraint is used to determine that at most one preparation action can be performed on the same holding arc, and the drone cannot take off and land at the same time to ensure that the model can correctly arrange the takeoff and landing sequence of the drone on the logistics cabinet to avoid docking conflicts, thereby ensuring the order of operations; the second takeoff and landing constraint satisfies the expression: 。 In some embodiments, the present application provides a drone path planning system 300 based on a smart logistics cabinet, such as Figure 4 As shown, the UAV path planning system 300 may include the following modules: An acquisition module 310 is used to acquire a delivery task set and calculate the total travel time corresponding to all delivery tasks in the delivery task set to determine a scheduling time range; A grouping module 320 is used to group the delivery task set to obtain multiple task groups, and the starting logistics cabinet and the end logistics cabinet of the delivery tasks in the same task group are the same; A constraint configuration module 330, used to configure constraint conditions for modeling, wherein the constraint conditions include target constraints representing completion time, time constraints, path constraints, task constraints, variable constraints, and takeoff and landing constraints; A construction module 340 is used to introduce binary variables and use a time extension network method to introduce a time dimension to construct a goal planning model with the goal of minimizing the completion time, so as to transform the UAV task scheduling process into an arc selection process in a time extension network; The solving module 350 is used to solve the target planning model through a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets.

[0057] According to an embodiment of the present application, any multiple modules among the acquisition module 310, the grouping module 320, the constraint configuration module 330, the construction module 340 and the solution module 350 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0058] In some embodiments, the construction module 340 may be specifically used to: Discretize the scheduling time range into multiple time points with sufficiently small intervals and equal intervals; Create a node based on each logistics cabinet and each time point to obtain multiple nodes connected by arcs; For each time point, a holding arc is created for each logistics cabinet, and a flight arc is created from one logistics cabinet to another to represent the flight path of the drone from one logistics cabinet to another. Both the flight arc and the holding arc have corresponding head nodes and tail nodes. Introduce binary variables to represent whether each arc is selected. When Selected, when When Not selected.

[0059] In some embodiments, the solution module 350 may be specifically used to: When selecting a flight arc In the case of, based on the preset first strategy and second strategy, the holding arc is mapped to each flight arc, and the arc connected to the flight arc is selected. Multiple preceding holding arcs of the head node, and connected to the arcs Multiple subsequent holding arcs of the tail node; The first strategy includes: for the flight arc , add a set of preceding holding arcs , in the selection arc In the case of The corresponding holding arc is determined in order to represent the take-off action of the UAV on the corresponding logistics cabinet; The second strategy includes: Append a set of subsequent hold arcs , in the selection arc In the case of The corresponding holding arc is determined in order to characterize the landing action of the UAV on the corresponding logistics cabinet.

[0060] Figure 4 Each module in the system shown has the function of implementing each step in the aforementioned drone path planning method based on intelligent logistics cabinets, and can achieve its corresponding technical effects. For the sake of concise description, it will not be repeated here.

[0061] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 5 shown.

[0062] The electronic device may include a processor 410 and a memory 420 storing computer program instructions.

[0063] Specifically, the processor 410 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0064] The memory 420 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 420 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 420 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 420 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 420 is a non-volatile solid-state memory.

[0065] The memory 420 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, typically, the memory 420 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the above-mentioned embodiments of the method for drone path planning based on a smart logistics cabinet.

[0066] The processor 410 implements any one of the drone path planning methods based on the smart logistics cabinet in the above embodiments by reading and executing computer program instructions stored in the memory 420.

[0067] In one example, the electronic device may further include a communication interface 430 and a bus 400. Figure 5 As shown, the processor 410, the memory 420, and the communication interface 430 are connected via a bus 400 and communicate with each other.

[0068] The communication interface 430 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application. Bus 400 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 400 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0069] In addition, in combination with the drone path planning method based on the smart logistics cabinet in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the drone path planning methods based on the smart logistics cabinet in the above embodiment is implemented.

[0070] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0071] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0072] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0073] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0074] In summary, compared with the prior art, this application has the following beneficial effects: 1. This application conducts analysis with the goal of minimizing the completion time, introduces binary variables and uses the time extension network method to introduce the time dimension to construct a target planning model, transforms the task scheduling of drones into an arc selection problem in a time extension network, and performs task planning based on nodes and arcs without the need to use task drone indexes or task indexes; creates a suitable scheduling plan for drones to ensure that all tasks are completed and there are no drone docking conflicts on each logistics cabinet.

[0075] 2. The target programming model is a time-extended network model. By discretizing time, the dynamic problem is transformed into a static network problem, making the problem more intuitive, easier to understand and analyze. The target programming model is easy to expand, and the time granularity can be adjusted or new constraints can be added according to actual needs. It can transform problems that are difficult to model with continuous time models into problems that can be modeled, thus achieving modeling and solving of complex problems.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone path planning method based on an intelligent logistics cabinet, characterized in that: include: Obtain a delivery task set, and calculate the total travel time corresponding to all delivery tasks in the delivery task set to determine the scheduling time range; The delivery task set is grouped to obtain a plurality of task groups, wherein the starting point logistics cabinet and the end point logistics cabinet of the delivery tasks in the same task group are the same; configuring constraints for modeling, wherein the constraints include target constraints representing completion time, time constraints, path constraints, task constraints, variable constraints, and takeoff and landing constraints; With the goal of minimizing the completion time, binary variables are introduced and the time dimension is introduced using the time-extended network method to construct a goal programming model, so as to transform the UAV task scheduling process into an arc selection process in the time-extended network. The target planning model is solved by a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets.

2. The drone path planning method based on the intelligent logistics cabinet according to claim 1 is characterized in that: The goal of minimizing the completion time is to introduce binary variables and use the time extension network method to introduce the time dimension to build a target programming model, including: Discretizing the scheduling time range into a plurality of time points with sufficiently small intervals and equal intervals; Creating a node based on each logistics cabinet and each time point to obtain a plurality of nodes connected by arcs; For each time point, a holding arc is created for each logistics cabinet, and a flight arc is created from one logistics cabinet to another logistics cabinet to represent the flight path of the drone from one logistics cabinet to another logistics cabinet; the flight arc and the holding arc both correspond to a head node and a tail node; Introduce binary variables to represent whether each arc is selected. When Be chosen, represents the index of the arc, is the set of all arcs, when When Not selected.

3. The drone path planning method based on the intelligent logistics cabinet according to claim 1 is characterized in that: The target planning model is solved by a preset solver to obtain a plurality of flight arcs and holding arcs to determine a path planning network for the drone to perform a delivery task between a plurality of smart logistics cabinets, including: When selecting a flight arc In the case of, based on the preset first strategy and second strategy, the holding arc is mapped to each flight arc, and the arc connected to the flight arc is selected. Multiple preceding holding arcs of the head node, and connected to the arcs Multiple subsequent holding arcs of the tail node; is the flight arc set, Represents the index of the arc; The first strategy includes: for the flight arc , add a set of preceding holding arcs , in the selection arc In the case of The corresponding holding arc is determined in order to represent the take-off action of the UAV on the corresponding logistics cabinet; The second strategy includes: Append a set of subsequent hold arcs , in the selection arc In the case of The corresponding holding arc is determined in order to characterize the landing action of the UAV on the corresponding logistics cabinet.

4. The drone path planning method based on the intelligent logistics cabinet according to claim 3 is characterized in that: For time point , if there is a time point , so that , then at the node and Create a flight arc between ,in, Indicates that the drone arrive Flight time; Indicates the starting point logistics cabinet, Indicates the destination logistics cabinet. , , Assemble for logistics cabinets; The path planning network satisfies the expression: ,in, planning a network for the path, Represents a collection of nodes. represents a collection of arcs, and Represent the collection of holding arcs and flying arcs respectively.

5. The drone path planning method based on the intelligent logistics cabinet according to claim 2 is characterized in that: The completion time of the multiple task groups includes the take-off action preparation time and the landing action preparation time of the drone; when the number of configured flight arcs from the starting logistics cabinet to the end logistics cabinet is greater than or equal to the number of tasks in the task group, it is determined that all tasks can be executed; The target constraint satisfies the expression: ,in, represents the maximum time required for all drones to complete their assigned tasks; The time constraint satisfies the expression: In the formula, It is an arc The tail node of For Node The corresponding time point is used to indicate that the drone is at the node Corresponding logistics cabinet The time to complete the landing, is a binary variable, Indicates the preparation time required to complete a flight action. The corresponding flight arc represents the UAV performing the corresponding flight action; the time constraint guarantees At least with any selected flight arc The completion time is the same.

6. The drone path planning method based on the intelligent logistics cabinet according to claim 5 is characterized in that: The path constraints include a first path constraint, a second path constraint, a third path constraint and a fourth path constraint; The first path constraint satisfies the expression: In the formula, Represents a node, And it means drone The starting node of , represents the index of the drone, Indicates the logistics cabinet where the drone initially stops. It is a collection of drones. Represents all outgoing nodes The first path constraint is used to ensure that each drone starts to perform the task with the logistics cabinet where it initially stops as the starting node; The second path constraint satisfies the expression: In the formula, Indicates the inflow node The arc set of , Indicates that except for the source node and precipitation nodes The set of intermediate nodes other than Represents the total number of nodes; the second path constraint is used to ensure that for each intermediate node ,The number of its inflow arcs must be equal to the number of outflow arcs, i.e. the drone must leave after reaching an intermediate node to ensure the conservation of the number of drones; The third path constraint satisfies the expression: The third path constraint is used to ensure that for each intermediate node , at most one arc flowing into it is selected to ensure that the UAV can only choose one path to execute at any intermediate node, avoiding multiple paths to ensure the uniqueness and certainty of the path; The fourth path constraint satisfies the expression: The fourth path constraint is used to ensure that the UAV eventually flows into the settling node and ends its flight action, and ensures that the number of all arcs flowing into the settling node must be equal to the total number of UAVs, and that each UAV can be effectively scheduled.

7. The method for drone path planning based on smart logistics cabinet according to claim 5, characterized in that: The variable constraint is used to define the value range of the variable and satisfies the expression: in, , , are binary variables, represents an arc, represents a collection of arcs, The corresponding holding arc indicates that the drone stays on the corresponding logistics cabinet for a period of time; The task constraints satisfy the expression: In the formula, Represents a task group, Represents the task group The corresponding arc set, represents the set of all task groups, Represents the number of tasks in a task group; the task constraint is used to ensure that each task group The number of selected flight arcs is at least equal to the number of each task group The number of tasks to ensure that all tasks are executed; The takeoff and landing constraints include a first takeoff and landing constraint and a second takeoff and landing constraint; wherein the first takeoff and landing constraint is used to determine that if a flight arc is selected, an additional holding arc must be selected to ensure that the takeoff and landing actions must be performed together with the execution flight segment; the first takeoff and landing constraint satisfies the expression: In the formula, , and is a binary variable, indicating that if the arc is maintained If selected, it is equal to 1, otherwise it is equal to 0; is the set of holding arcs connected to the flying arc head node, is the set of holding arcs connected to the tail node of the flight arc; and Used to distinguish between flying arc and holding arc, and This means that if a drone flies, it must take off and land. Used to distinguish the types of arcs, and Respectively belong and ; The second takeoff and landing constraint is used to determine that at most one preparation action can be performed on the same holding arc, and the drone cannot take off and land at the same time to ensure that the model can correctly arrange the takeoff and landing sequence of the drone on the logistics cabinet to avoid docking conflicts, thereby ensuring the order of operations; The second takeoff and landing constraint satisfies the expression: 。 8. A drone path planning system based on intelligent logistics cabinet, characterized in that: include: An acquisition module is used to acquire a delivery task set and calculate the total travel time corresponding to all delivery tasks in the delivery task set to determine a scheduling time range; A grouping module, used for grouping the delivery task set to obtain a plurality of task groups, wherein the starting point logistics cabinet and the end point logistics cabinet of the delivery tasks in the same task group are the same; A constraint configuration module, used to configure constraint conditions for modeling, wherein the constraint conditions include target constraints representing completion time, time constraints, path constraints, task constraints, variable constraints, and takeoff and landing constraints; A building module is used to introduce binary variables and the time dimension using the time-extended network method to construct a goal planning model with the goal of minimizing the completion time, so as to transform the UAV task scheduling process into an arc selection process in the time-extended network; A solving module is used to solve the target planning model through a preset solver to obtain multiple flight arcs and holding arcs to determine a path planning network for the drone to perform delivery tasks between multiple smart logistics cabinets.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the method for drone path planning based on a smart logistics cabinet as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the drone path planning method based on the smart logistics cabinet as described in any one of claims 1 to 7 is implemented.

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